Technical Guide
Scalable, Intelligent, and Integration-ready
Scalable, Intelligent, and Integration-ready
The Data Quality Navigator (DQN) is a web-based application which helps to identify and correct data quality errors. The application is delivered via a SaaS model, and it is hosted with cloud infrastructure, typically Microsoft Azure. The application uses advanced algorithms and AI models to identify data errors and to propose remediation actions. The application backend consists of a collection of independent microservices.
DQN connects easily with your entire ecosystem whether through direct system connectors (e.g., SAP, IFS), database access (e.g., SQL Server, Oracle), file-based inputs (e.g., CSV, Excel, PDF), or external API connections (e.g., Melissa, Dun & Bradstreet). If a cloud setup is not an option, secure on-premise connectors are also available.
Each customer operates in a single-tenant environment with an isolated resource stack. This allows us to tailor configurations to strict industry-specific standards. Our operations follow ISO-certified practices, ensuring full compliance with all relevant security and regulatory requirements.
The DQN platform is built on a state-of-the-art microservices architecture, deployed on secure cloud infrastructure such as Microsoft Azure. Each client operates in a dedicated single-tenant instance, ensuring high scalability, performance, and data isolation. We follow cloud-native best practices, leveraging modern technologies like Kubernetes, Angular, and .NET Core. We integrate seamlessly with cutting-edge enterprise ecosystems, including Snowflake, Databricks, Azure Data Factory, and Amazon S3 ensuring flexibility, speed, and future-ready scalability for demanding data-driven organizations.
Integrate data from internal systems, external APIs, and unstructured documents through robust connectors and parsing capabilities.
Leverage advanced AI models and curated validation rules to identify data quality issues, suggest validation rules, and profile your data intelligently.
AI-powered agents automate data validation, correction, enrichment, and matching; all managed in centralized workbenches for full control.
Ensure transparency and compliance with multi-level monitors, detailed audit trails, role-based access, and flexible approval workflows.
